## # A tibble: 9 × 3
## # Groups: type [3]
## type month mean_value
## <chr> <dbl> <dbl>
## 1 both 1 14.5
## 2 both 2 13.4
## 3 both 12 13.1
## 4 commercial 1 13.1
## 5 commercial 7 13.1
## 6 commercial 12 12.7
## 7 oscar 1 12.0
## 8 oscar 2 11.6
## 9 oscar 3 11.0
## # A tibble: 25 × 3
## # Groups: cluster [5]
## name cluster distance
## <chr> <int> <dbl>
## 1 Chris Hemsworth 1 0.222
## 2 Nicolas Cage 1 0.232
## 3 Lyna 1 0.234
## 4 Sam Elliott 1 0.240
## 5 Mads Mikkelsen 1 0.248
## 6 Maria Bakalova 2 0.0219
## 7 Chadwick Boseman 2 0.0225
## 8 Irrfan Khan 2 0.0248
## 9 Jean Dujardin 2 0.0275
## 10 Carrie Fisher 2 0.0293
## # ℹ 15 more rows
## # A tibble: 5 × 3
## # Groups: cluster, nominee [5]
## cluster nominee prop_nominees
## <int> <dbl> <dbl>
## 1 1 1 0.246
## 2 2 1 0.454
## 3 3 1 0.408
## 4 4 1 0.222
## 5 5 1 0.298
## Error in ar.burg.default(x, aic = aic, order.max = order.max, na.action = na.action, :
## zero-variance series
## Error in ar.burg.default(x, aic = aic, order.max = order.max, na.action = na.action, :
## zero-variance series
## Error in ar.burg.default(x, aic = aic, order.max = order.max, na.action = na.action, :
## zero-variance series
## Error in if (order) coefs[order, 1L:order] else numeric() :
## argument is not interpretable as logical
## Error in ar.burg.default(x, aic = aic, order.max = order.max, na.action = na.action, :
## zero-variance series
## Error in ar.burg.default(x, aic = aic, order.max = order.max, na.action = na.action, :
## zero-variance series
## Error in ar.burg.default(x, aic = aic, order.max = order.max, na.action = na.action, :
## zero-variance series
## Error in ar.burg.default(x, aic = aic, order.max = order.max, na.action = na.action, :
## zero-variance series
##
## 0 1
## 317 317
##
## 0 1
## 317 317
##
## Call:
## randomForest(formula = nominee ~ ., data = train_data)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 4
##
## OOB estimate of error rate: 28.06%
## Confusion matrix:
## 0 1 class.error
## 0 180 73 0.2885375
## 1 69 184 0.2727273
## Actual
## Predicted 0 1
## 0 46 19
## 1 18 45
## [1] "Accuracy: 0.7109375"
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 46 19
## 1 18 45
##
## Accuracy : 0.7109
## 95% CI : (0.6242, 0.7876)
## No Information Rate : 0.5
## P-Value [Acc > NIR] : 1.003e-06
##
## Kappa : 0.4219
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.7188
## Specificity : 0.7031
## Pos Pred Value : 0.7077
## Neg Pred Value : 0.7143
## Prevalence : 0.5000
## Detection Rate : 0.3594
## Detection Prevalence : 0.5078
## Balanced Accuracy : 0.7109
##
## 'Positive' Class : 0
##
## MeanDecreaseGini
## trend 15.317558
## spike 13.083589
## linearity 55.017603
## curvature 23.670419
## e_acf1 13.348541
## e_acf10 11.671731
## entropy 10.823001
## x_acf1 12.192964
## x_acf10 12.718348
## diff1_acf1 16.797037
## diff1_acf10 12.562506
## diff2_acf1 13.624891
## max_spike_height 11.072804
## nominated_previously 2.918732
## age 12.382291
## gender 1.575557
## american 1.636712
## cluster 10.202593
## won_previously 1.688494
##
## Call:
## randomForest(formula = nominee ~ ., data = train_data)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 28.46%
## Confusion matrix:
## 0 1 class.error
## 0 182 71 0.2806324
## 1 73 180 0.2885375
## Actual
## Predicted 0 1
## 0 46 17
## 1 18 47
## [1] "Accuracy: 0.7265625"
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 46 17
## 1 18 47
##
## Accuracy : 0.7266
## 95% CI : (0.6408, 0.8016)
## No Information Rate : 0.5
## P-Value [Acc > NIR] : 1.492e-07
##
## Kappa : 0.4531
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.7188
## Specificity : 0.7344
## Pos Pred Value : 0.7302
## Neg Pred Value : 0.7231
## Prevalence : 0.5000
## Detection Rate : 0.3594
## Detection Prevalence : 0.4922
## Balanced Accuracy : 0.7266
##
## 'Positive' Class : 0
##
## MeanDecreaseGini
## trend 17.644047
## spike 14.426482
## linearity 55.662106
## curvature 25.216108
## e_acf1 14.118445
## e_acf10 13.641810
## entropy 15.322161
## x_acf1 15.472704
## x_acf10 13.446183
## diff1_acf1 15.663549
## diff1_acf10 14.007693
## diff2_acf1 14.177337
## max_spike_height 13.110981
## cluster 9.766231
